Fine- and coarse-scale movements and habitat use by Wood Turtles (<i>Glyptemys insculpta</i>) based on probabilistic modeling of radiotelemetry and GPS-telemetry data
Bibliographic record
Abstract
Understanding animal movement and habitat use is critical for the delineation of habitat requiring protection for species at risk. Defining critical habitat requires studies with observations at a fine enough scale to reflect how animals use and move among habitats and include enough individuals to generalize findings to the population. We present results of a multiyear study on 48 adult Wood Turtles (Glyptemys insculpta (Le Conte, 1830)) from two different populations monitored with low-frequency radiotelemetry and high-frequency GPS telemetry. Results demonstrated the propensity for conventional radiotelemetry to underestimate cumulative distances moved and overestimate the amount of habitat used by Wood Turtles. Together the two data sets demonstrate the propensity for Wood Turtles to remain in close proximity to the river and that some differences in habitat use occur between the sexes; males tended to move parallel to the river, whereas females moved perpendicular to the river. The GPS-telemetry data provided a robust spatiotemporal data set that provided a better understanding of frequently used habitat types and features. Overall, study results suggest that currently delineated areas of protected habitat are likely to be effective in conserving these two populations and provides significantly improved, spatially explicit knowledge that can be used to inform further mitigation efforts if necessary.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".